X-ray films to be analyzed by Computer Aided Detection (CAD) are scanned by a digitizer station, and converted into digital format. Computer algorithms analyze the digital images and identify features of the digitized image such as suspicious areas in the case of mammography for example.
In the diagnosis process, current methods require that the physician first reads the mammograms without the aid of a computer. Once the physician has noted her findings, she turns on computer monitors that show suspicious areas identified by CAD analysis as an overlay over the digitized versions of the mammogram. The suspicious areas may confirm the findings of the physician. Or, the physician may be prompted to re-examine areas she had not found to be suspicious, but which were highlighted by the computer.
These suspicious areas are typically highlighted using “marks” on the images indicating the vicinity of a suspicious region. In the event that these CAD marks disagree with the physician's findings, she is then confronted with the task of determining why her findings differ from the CAD marks. Often, the CAD mark is due to some artifact or other obvious attribute of the image. Other times, the reason for the mark is more subtle. It can be very unsettling for the physician if the reason for the discrepancy cannot be easily determined.
Current systems attempt to mark regions that the computer “thinks” are cancerous. Therefore, great care must be taken to train the CAD system to only mark regions that have a high probability of being cancerous, and minimize the number of false-positives. The trade-off is that this results in the computer not marking a significant percentage of regions that are potentially cancerous (false-negatives). Current systems balance this tradeoff by establishing a “likelihood” for whether a detected region is cancerous. The threshold for this likelihood is then set to optimize performance on the ROC (receiver operating characteristics) curve for sensitivity vs. specificity. In addition, certain heuristics can be added to discard potential false-positives. Any detected region which does not score high enough on the likelihood scale, or which fails certain heuristic tests, is discarded and no mark is shown.
This leads to what is referred to as the “second read” model, based on the idea of using two independent analyses of the images (one by a human and one by the computer) to improve the chances of correctly identifying potential cancers in the image. The human correctly identifies a certain percentage of the suspicious regions, the computer correctly identifies a certain percentage (some overlapping, some not), with a final result being a higher overall detection rate.
A major downside to this approach is that it requires the physician to read each image twice: once with the CAD marks and once without. A second downside is that once the physician views the CAD results, the human and CAD results may disagree. It is then the responsibility of the physician to determine the reason for the discrepancy and to determine whether the computer or the physician is correct. Even though some systems may display the CAD results with a probability or likelihood that a suspicious area is cancerous (see for example Zheng et al. 2001, Radiology, 221(3):633-640 and U.S. Patent Applications No. 20020097902, and 20020076091), the CAD results displayed by the computer remain of a “pass/fail” nature and the physician may often struggle in determining the underlying reason for the discrepancy.